Dynamic testing of systems

ABSTRACT

Aspects of the invention include receiving system data associated with a first system, the first system comprising a plurality of system components, wherein the system data comprises component data for each system component in the plurality of system components, obtaining historical performance data for each system component in the plurality of system components, determining at least one testing constraint associated with the first system, determining a test environment for the first system, the test environment comprising a plurality of test cases for the first system based on the system data, the historical performance data, and the at least one testing constraint, and executing the test environment on the first system.

BACKGROUND

The present invention generally relates to testing computer systems, andmore specifically, to a dynamic testing of computer systems.

Computerized devices control almost every aspect of our life—fromwriting documents to controlling traffic lights. However, computerizeddevices can be error prone, and thus require a testing phase in whichthe errors, or bugs, should be discovered. The testing phase isconsidered one of the most difficult tasks in designing a computerizeddevice. The cost of not discovering a bug may be enormous, as theconsequences of the bug may be disastrous. Additionally, a bug inhardware or firmware may be expensive to fix if it is discovered afterthe computerized device has shipped to customers, as patching it mayrequire call-back of the computerized device. Hence, many developers ofcomputerized devices invest a substantial portion of the developmentcycle to discover erroneous behaviors of the computerized device.

During the testing phase a system under test (SUT) is being tested. TheSUT may be, for example, a computer program, a hardware device,firmware, an embedded device, a component thereof, or the like. Testingmay be performed using a test suite that includes test cases. The testsuite may be reused to revalidate that the SUT exhibits a desiredfunctionality with respect to the tests of the test suite. For example,the test suite may be reused to check that the SUT works properly aftera bug is fixed. The test suite may be used to check that the bug isindeed fixed (with respect to a test that previously induced theerroneous behavior). Additionally, or alternatively, the test suite maybe used to check that no new bugs were introduced (with respect to othertests of the tests suite that should not be affected by the bug fix).

SUMMARY

Embodiments of the present invention are directed to methods for dynamictesting of systems. A non-limiting example computer-implemented methodincludes receiving system data associated with a first system, the firstsystem comprising a plurality of system components, wherein the systemdata comprises component data for each system component in the pluralityof system components, obtaining historical performance data for eachsystem component in the plurality of system components, determining atleast one testing constraint associated with the first system,determining a test environment for the first system, the testenvironment comprising a plurality of test cases for the first systembased on the system data, the historical performance data, and the atleast one testing constraint, and executing the test environment on thefirst system.

Other embodiments of the present invention implement features of theabove-described method in computer systems and computer programproducts.

Additional technical features and benefits are realized through thetechniques of the present invention. Embodiments and aspects of theinvention are described in detail herein and are considered a part ofthe claimed subject matter. For a better understanding, refer to thedetailed description and to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The specifics of the exclusive rights described herein are particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other features and advantages ofthe embodiments of the invention are apparent from the followingdetailed description taken in conjunction with the accompanying drawingsin which:

FIG. 1 depicts a block diagram of a system for dynamic testing ofcomputer systems according to one or more embodiments of the invention;

FIG. 2 depicts of block diagram of three system configurations andassociated test case selection according to one or more embodiments;

FIG. 3 depicts a block diagram of execution of the learning test case ona system under test according to one or more embodiments of theinvention;

FIG. 4 depicts a flow diagram of a method for dynamic testing of systemsaccording to one or more embodiments of the present invention;

FIG. 5 depicts a cloud computing environment according to one or moreembodiments of the present invention;

FIG. 6 depicts abstraction model layers according to one or moreembodiments of the present invention; and

FIG. 7 depicts a computer system in accordance with one or moreembodiments of the present invention.

The diagrams depicted herein are illustrative. There can be manyvariations to the diagrams or the operations described therein withoutdeparting from the spirit of the invention. For instance, the actionscan be performed in a differing order or actions can be added, deletedor modified. Also, the term “coupled” and variations thereof describeshaving a communications path between two elements and does not imply adirect connection between the elements with no interveningelements/connections between them. All of these variations areconsidered a part of the specification.

DETAILED DESCRIPTION

One or more embodiments of the present invention provide a dynamictesting system for computer systems and/or mainframes that utilizesinformation gathered from a variety of sources to determine which testcases to execute and for how long to execute said test cases. Thedynamic testing system utilizes information gathered from sources suchas, but not limited to: historical data, component data, and real-timedata. The historical data includes, but is not limited to, the number oftest failures, time to test failures, the types of failures, and/or afailure rate. This historical data is taken from past test casesexecuted on the same and/or similar systems, manufacturing test recordsof other similar systems, and/or field failure records of other similarsystems. The component data includes data associated with the vintage ofthe component which includes year, manufacturing lot, and the like. Thecomponent data can have historical data associated with performance ofthe specific vintage of the component. For example, a memory card frommanufacturing lot “A140” may have a particular failure mode associatedwith a certain number of initial program loads (IPLs) that would requiretesting in a specific configuration for that vintage of memory card. Thereal-time data includes, but is not limited to, frequency and voltagestability and temperature increase rates on systems and/or individualsystem components. Based on the above information, a testing algorithmexecuted by the dynamic testing system can make decisions about theamount of test time to employ and also, which test cases to execute.This allows the test cases used and the length of time that each testcase is run to be determined dynamically as a system enters test, ratherthan having a defined test process that is used for a given set ofsystems. Two systems that have exactly the same components with similarvintages could have different tests if the second one starts test afterthe first one has completed, because of cognitive system learning fromthe first test, or new field data pulled into the system. Advantages ofthis dynamic testing system include the ability to identify potentialcost reductions in non-recurring engineering (NRE) costs and new productintroduction (NPI) life cycle costs associated with testing the systemsand system components.

NRE costs refer to the one-time cost of research, design, development,and testing a new product or product enhancement. When budgeting for anew product, NRE must be considered to analyze if a new product will beprofitable. Even though a company will pay for NRE on a project onlyonce, NRE costs can be prohibitively high and the product will need tosell well enough to produce a return on the initial investment. NRE isunlike production costs, which must be paid constantly to maintainproduction of a product. One major NRE cost is the testing resourcesneeded before shipping a product to a customer. When working with highlycustomizable products, such as mainframe computing systems, the testingphases before shipment to the customer is a costly enterprise. Forexample, a custom mainframe could be tailored to provide for high memoryand I/O usage which would require a variety of components to meet theseneeds. The mainframe can be built with memory components taken from avariety of suppliers who each provide the memory having differentvintages (e.g., different manufacturing lots, release years, and thelike). The performance of each of these memory components can bedifferent for each vintage. The dynamic testing system can take thismemory vintage information into consideration when determining what testcase to execute as well as how long to execute a test case on thespecific component. Due to the wide range of configurations that acustomer may need, the testing of these systems must be tailored to thespecific components, test history, and real-time testing conditions in atest environment. Constraints on testing must also be factored in giventhat there is limited time and test resources allocated for testingbefore a customer order must be shipped to the customer. As such, testengineers are challenged to provide the broadest test coverage for acustomer system given a test budget to help reduce the overall NREcosts.

One or more embodiments of the invention address the above problem byproviding the dynamic test system that maximizes test coverage whiletaking into consideration the limited time budget and NRE cost budget.The dynamic test system builds a test suite of test cases for a systemunder test as well as determines a testing time for each test case basedon the component data, historical testing data, and real-time datacollected during test execution.

FIG. 1 depicts a block diagram of a system for dynamic testing ofcomputer systems according to one or more embodiments of the invention.The system 100 includes a test controller 102 in electroniccommunication with a system under test 120. The test controller 102 cancommunicate with the system under test through a wired and/or wirelessnetwork 135. The system under test 120 includes a variety of components106 a-106N that are built to order based primarily on customerconfiguration requirements for the system 120. The components 106 a-106Ncan include, but are not limited to, processor cores, memory, storagedevices, battery modules, cooling systems, I/O devices, and the like.Each of these components 106 a-106N are selected based on the customerneed and customer pricing requirements. The configuration for thissystem, 101, is provided to the test controller 102 so a dynamic testdecision can be made. The system 100 also includes a knowledge base 140that includes data about each of the components 106 a-106N of the systemunder test 120 as well as historical data related to previous testcases, failure rates, failure modes, error types, and the like. Thesystem 100 also includes a test case database 104 that includes avariety of test cases that can be selected by the test controller 102for execution on the system under test 120. While the system under test120 can be any type of computer system, for ease of description, thesystem under test 120 described herein will be a mainframe computingsystem.

In one or more embodiments, a mainframe computing system is built basedon a variety of customer requirements and an example of the system undertest 120. In one example, a customer may request a low memory, high I/Oconfiguration. Further, the customer has a budget for said mainframewhich drives the determination of available testing time and testingresources (e.g., test engineers, etc.) that are available for testingthe mainframe. Further, the customer may have a shipping daterequirement that drives the date that testing must be completed whichfurther determines what testing time and resources are available forthis particular system. For example, the mainframe testing budget may beeight (8) hours of testing and must be completed before a selected datewith only two (2) test engineers available. When the mainframe is builtand sent for testing, the test controller 102 can obtain data associatedwith the components 106 a-106N in the mainframe from the knowledge base140. Based on this data, the test controller 102 can select a set oftest cases from the test case database 104 and/or built one or morecustom test cases for execution. Taking into consideration the time andbudget constraints 130, while also calculating fail probability and timeto fail projections for components 106 a to 106N based on the systemconfiguration 101 and knowledge base 140, the test controller 102 buildsa test environment for execution on the system under test 120(mainframe). The test environment includes what test cases are beingexecuted and for how long these test cases are being executed. The testcontroller 102 also determines based on time and budget constraints ifthe learning test case 302 can be invoked, shown in FIG. 3 .

In one or more embodiments of the invention, the component data takenfrom the knowledge base includes manufacturer data for each component106 a-106N. The manufacturer data includes historical failure rates forthe individual components based on historical tests from themanufacturer and also include the vintage of the component whichincludes version number, manufacturing year, failure rates/modes, etc.The historical failure rates include the test type for the component andthe test time, number of failures in the test steps, and the like. Inaddition, historical testing data taken from previous tests by the testcontroller 102 can be stored in the knowledge base 140. Similar to themanufacturer data, the historical testing data includes test casesexecuted on same and/or similar components as well as the testing times,the failure rates, failure modes, and number of failures in the teststeps.

FIG. 2 depicts a block diagram of three system configurations andassociated test case selection according to one or more embodiments. Theconfigurations 202 a, 202 b, 202 c refer to System A, System B, andSystem C. System A 202 a utilizes components Card PN 1 with amanufacturing date of Nov. 5, 2019 and Drive PN 2 with a manufacturingdate of Sep. 28, 2019. The test controller 102 can obtain component datafor each of these specific components to determine what test cases torun. This component data can include manufacturer vintage informationfor parts, failure rates, failure modes as wells as the manufacturinghistory for similar parts including test times, number of failures inthe test steps and/or include development test results for eachcomponent. Based on this, test environment 204 a for System A includes,for example, Test cases 3, 7, 10, 11, and 13. System B 202 b includescomponents Card PN 1 with a manufacturing date of Dec. 7, 2019 and DrivePN 2 with a manufacturing date of Oct. 1, 2019. Based on thesecomponents, the test controller 102 can determine the testingenvironment 204 to include all the test cases from system A and add testcase 4, test step 5, add 20 minutes to the testing based on thecharacterization data for this particular drive PN 2. System C 202 cincludes components card PN 1 manufactured on Oct. 25, 2019 and drive PN2 manufactured on Dec. 1, 2019. The test environment 204 c for System Ccan include all the test cases for System A with added test case 12,remove test case 7, and add test steps 1 and 2 based on the componentcharacterization data. Each system 202 a, 202 b, 202 c includes the sametype of components but given the different vintage (manufacturing date,lot number, etc.) of each component, the test environment changes foreach system.

In one or more embodiments of the invention, the example systems in FIG.2 illustrate the test environment changes based on similar systems withdifferent component vintages. Based on the test environments needed forthe systems, the test controller 102 can also schedule testing for avariety of systems by lining up specific resources for testing a system.In the FIG. 2 examples, because the three systems are using the sametest cases, these three may be scheduled for testing one after another.This can further maximize testing efficiency and reduce NRE costs basedon available resources. Embodiments of the invention enable threebenefits. First, it allows for better target testing so reliabilitygoals can be met. Second, it allows for determining that testing can bereduced for a system with a given set of components and still have aconfidence level that reliability requirements can be met. Third, itallows for justification for up front testing to be added to help reducethe cost of part failures for customers. For example, there is theoption to build 2 similar systems with the same set of components exceptone component is different between the two orders. The test time wouldbe different based on that one different component (it could bedifferent PN and/or part of a different lot). Based on historictest/field data on systems built up with similar components the testcontroller 102 can determine which one of the 2 similar systems wouldfinish tests earlier with the same confidence level for the reliabilitytargets (or finish at the same time with higher confidence level) andthe test controller 102 can determine which system would have a higherchance to fail because of that one different component. Because of thisthe test controller 102 can choose to build up the system that wouldneed less time or have higher confidence level. So the test controller102 can reduce testing time because of better component and evade apotential failure on test (because of the worse component), that couldadd more test time because of replacing component and possible retest ofnew component in the system. This not only reduces cost by saving time,but it can also allow for meeting schedules if there is limited testresources and a need to decide which system to start first.

Further, in one or more embodiments of the invention, the examples ofFIG. 2 illustrate building test environments for the three systems basedon components. The test controller 102 can determine the testingenvironment utilizing a machine learning model to identify the testcases and testing time. The machine learning model can build featurevectors including a plurality of features derived from historical testcase results for various components and component vintages. The featurevectors can be plotted in a multi-variate space and when a new systemconfiguration is presented for testing. The machine learning model canbuild a new feature vector for the new system and plot this new featurevector in the multi-variate space to determine a set of test cases andtest times and any other test environment conditions for the new system.The test controller 102 can execute and implement one or more so-calledclassifiers (described in more detail below). In one or more embodimentsof the invention, the features of the various classifiers describedherein can be implemented on the processing system 700 shown in FIG. 7 ,or can be implemented on a neural network (not shown).

In one or more embodiments of the invention, the features of theclassifiers can be implemented by configuring and arranging theprocessing system 700 to execute machine learning (ML) algorithms. Ingeneral, ML algorithms, in effect, extract features from received data(e.g., inputs to the classifiers) in order to “classify” the receiveddata. Examples of suitable classifiers include but are not limited toneural networks (described in greater detail below), support vectormachines (SVMs), logistic regression, decision trees, hidden MarkovModels (HMIs), etc. The end result of the classifier's operations, i.e.,the “classification,” is to predict a class for the data. The MLalgorithms apply machine learning techniques to the received data inorder to, over time, create/train/update a unique “model.” The learningor training performed by the classifiers can be supervised,unsupervised, or a hybrid that includes aspects of supervised andunsupervised learning. Supervised learning is when training data isalready available and classified/labeled. Unsupervised learning is whentraining data is not classified/labeled so must be developed throughiterations of the classifier. Unsupervised learning can utilizeadditional learning/training methods including, for example, clustering,anomaly detection, neural networks, deep learning, and the like.

In embodiments of the invention where the classifiers are implemented asneural networks, a resistive switching device (RSD) can be used as aconnection (synapse) between a pre-neuron and a post-neuron, thusrepresenting the connection weight in the form of device resistance.Neuromorphic systems are interconnected processor elements that act assimulated “neurons” and exchange “messages” between each other in theform of electronic signals. Similar to the so-called “plasticity” ofsynaptic neurotransmitter connections that carry messages betweenbiological neurons, the connections in neuromorphic systems such asneural networks carry electronic messages between simulated neurons,which are provided with numeric weights that correspond to the strengthor weakness of a given connection. The weights can be adjusted and tunedbased on experience, making neuromorphic systems adaptive to inputs andcapable of learning. For example, a neuromorphic/neural network forhandwriting recognition is defined by a set of input neurons, which canbe activated by the pixels of an input image. After being weighted andtransformed by a function determined by the network's designer, theactivations of these input neurons are then passed to other downstreamneurons, which are often referred to as “hidden” neurons. This processis repeated until an output neuron is activated. Thus, the activatedoutput neuron determines (or “learns”) which character was read.Multiple pre-neurons and post-neurons can be connected through an arrayof RSD, which naturally expresses a fully-connected neural network. Inthe descriptions here, any functionality ascribed to the system 100 canbe implemented using the processing system 700 applies.

In one or more embodiments of the invention, the test controller 102 canbuild upon the knowledge base 140 by executing learning test cases.Should the test controller 102 determine that a testing environmentrequires a certain test time that is shorter than the available testingtime, the test controller 102 can use the remaining test time toexecuting these learning test cases on the system under test 120. Thelearning test cases are used to explore potential errors, failures,failure modes, and the like for the components 106 a-106N. The resultsof these learning test cases can be stored in the knowledge base 140 andbe further utilized to train the machine learning model described above.The learning test cases can be built to test outlier or “corner”conditions for the system under test 120. The specific SUT where thelearning test cases are being used on will not be changed, but the datais them used for future systems with similar components. The data isadded to the cognitive computing feedback loop. FIG. 3 depicts a blockdiagram of execution of the learning test case on a system under testaccording to one or more embodiments of the invention. The learning testcase 302 can be selected based on the system configuration 101 of thesystem under test and the component data 306 for the system, usinginformation from the test case database 104 and the knowledge base 140,also referenced in FIG. 1 , to select tests that may be likely to detectany potential defects in the SUT. The learning test case 302 can beexecuted and during testing, various parameters can be adjusted tocollect real-time running data. The parameters that are adjusted includevoltage and frequency adjustments 312 and system temperaturesadjustments 314. This real-time data can be collected and stored forlater analysis by the machine learning model. The machine learning modelcan use this data to later determine test execution environments andrecommendations 320 for future test. The clocking frequency of theprocessor and memory are monitored (along with temperature and partfailures) and the data is used as a part of the learning model. Thelearning model then may use this to adjust tests to better stress theparts and verify that they are customer-shippable. The temperature andfrequency aren't adjusted directly, but can be related to certain tests;for example, a stress test could cause temperature increases.

In one or more embodiments of the invention, the test controller 102 canutilize feedback from the test cases that are run on the system undertest. Based on a review of any failures that exist, the test controller102 can select one or more additional test cases to run on the systemunder test if there is any available time and/or resources. In addition,the test controller 102 can obtain data from systems that are in thefield with existing customers. This data can be referred to as “callhome” data that is received from these existing systems that send errorreports that would be used to select new test cases based on the sameand/or similar components for the system under test.

In one or more embodiments of the invention, the test controller 102 canselect additional test cases to run on the system under test when thereexists additional time and/or resources after the initial set of testcases are run. The test controller 102 can define a threshold level forfinding test cases that may exist outside a cluster of test cases. Thatis to say, when the test controller 102 utilizes a machine learningclustering algorithm, the threshold for selecting the test cases basedon the clustering algorithm can be adjusted to select one or more newtest cases that are available to run to attempt to find any fieldfailures based on data taken regarding the system under test components.

FIG. 4 depicts a flow diagram of a method 400 for dynamic testing ofsystems according to one or more embodiments of the invention. At leasta portion of the method 400 can be executed, for example, by the testcontroller 102 shown in FIG. 1 . The method 400 includes receivingsystem data associated with a first system, the first system comprisinga plurality of system components, wherein the system data comprisescomponent data for each system component in the plurality of systemcomponents, as shown in block 402. The component data includes vintageinformation from the manufacturer which includes failure rates, failuremodes, and the like. At block 404, the method 400 includes obtaininghistorical performance data for each system component in the pluralityof components. The historical performance data includes historic testcase results run on the same and/or similar components. Also, the method400, at block 406, includes determining at least one testing constraintassociated with the first system. The testing constraint can relate totesting time, testing resources, and/or any other NRE cost for theproduct. The method 400 further includes determining a test environmentfor the first system, the test environment comprising a plurality oftest cases for the first system based on the system data, the historicalperformance data, and the at least one testing constraint, as shown inblock 408. And at block 410, the method 400 includes executing the testenvironment on the first system.

Additional processes may also be included. It should be understood thatthe processes depicted in FIG. 4 represent illustrations, and that otherprocesses may be added or existing processes may be removed, modified,or rearranged without departing from the scope and spirit of the presentdisclosure.

In one or more embodiments of the invention, the controller 102 can beimplemented on the processing system 700 found in FIG. 7 . Additionally,the cloud computing system 50 can be in wired or wireless electroniccommunication with one or all of the elements of the system 100. Cloud50 can supplement, support or replace some or all of the functionalityof the elements of the system 100. Additionally, some or all of thefunctionality of the elements of system 100 can be implemented as a node10 (shown in FIGS. 5 and 6 ) of cloud 50. Cloud computing node 10 isonly one example of a suitable cloud computing node and is not intendedto suggest any limitation as to the scope of use or functionality ofembodiments of the invention described herein.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 5 , illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 6 , a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 5 ) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 6 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and a semi-virtualized, portable commandcenter 96.

Turning now to FIG. 7 , a computer system 700 is generally shown inaccordance with an embodiment. The computer system 700 can be anelectronic, computer framework comprising and/or employing any numberand combination of computing devices and networks utilizing variouscommunication technologies, as described herein. The computer system 700can be easily scalable, extensible, and modular, with the ability tochange to different services or reconfigure some features independentlyof others. The computer system 700 may be, for example, a server,desktop computer, laptop computer, tablet computer, or smartphone. Insome examples, computer system 700 may be a cloud computing node.Computer system 700 may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.Computer system 700 may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

As shown in FIG. 7 , the computer system 700 has one or more centralprocessing units (CPU(s)) 701 a, 701 b, 701 c, etc. (collectively orgenerically referred to as processor(s) 701). The processors 701 can bea single-core processor, multi-core processor, computing cluster, or anynumber of other configurations. The processors 701, also referred to asprocessing circuits, are coupled via a system bus 702 to a system memory703 and various other components. The system memory 703 can include aread only memory (ROM) 704 and a random access memory (RAM) 705. The ROM704 is coupled to the system bus 702 and may include a basicinput/output system (BIOS), which controls certain basic functions ofthe computer system 700. The RAM is read-write memory coupled to thesystem bus 702 for use by the processors 701. The system memory 703provides temporary memory space for operations of said instructionsduring operation. The system memory 703 can include random access memory(RAM), read only memory, flash memory, or any other suitable memorysystems.

The computer system 700 comprises an input/output (I/O) adapter 706 anda communications adapter 707 coupled to the system bus 702. The I/Oadapter 706 may be a small computer system interface (SCSI) adapter thatcommunicates with a hard disk 708 and/or any other similar component.The I/O adapter 706 and the hard disk 708 are collectively referred toherein as a mass storage 710.

Software 711 for execution on the computer system 700 may be stored inthe mass storage 710. The mass storage 710 is an example of a tangiblestorage medium readable by the processors 701, where the software 711 isstored as instructions for execution by the processors 701 to cause thecomputer system 700 to operate, such as is described herein below withrespect to the various Figures. Examples of computer program product andthe execution of such instruction is discussed herein in more detail.The communications adapter 707 interconnects the system bus 702 with anetwork 712, which may be an outside network, enabling the computersystem 700 to communicate with other such systems. In one embodiment, aportion of the system memory 703 and the mass storage 710 collectivelystore an operating system, which may be any appropriate operatingsystem, such as the z/OS or AIX operating system from IBM Corporation,to coordinate the functions of the various components shown in FIG. 7 .

Additional input/output devices are shown as connected to the system bus702 via a display adapter 715 and an interface adapter 716 and. In oneembodiment, the adapters 706, 707, 715, and 716 may be connected to oneor more I/O buses that are connected to the system bus 702 via anintermediate bus bridge (not shown). A display 719 (e.g., a screen or adisplay monitor) is connected to the system bus 702 by a display adapter715, which may include a graphics controller to improve the performanceof graphics intensive applications and a video controller. A keyboard721, a mouse 722, a speaker 723, etc. can be interconnected to thesystem bus 702 via the interface adapter 716, which may include, forexample, a Super I/O chip integrating multiple device adapters into asingle integrated circuit. Suitable I/O buses for connecting peripheraldevices such as hard disk controllers, network adapters, and graphicsadapters typically include common protocols, such as the PeripheralComponent Interconnect (PCI). Thus, as configured in FIG. 7 , thecomputer system 700 includes processing capability in the form of theprocessors 701, and, storage capability including the system memory 703and the mass storage 710, input means such as the keyboard 721 and themouse 722, and output capability including the speaker 723 and thedisplay 719.

In some embodiments, the communications adapter 707 can transmit datausing any suitable interface or protocol, such as the internet smallcomputer system interface, among others. The network 712 may be acellular network, a radio network, a wide area network (WAN), a localarea network (LAN), or the Internet, among others. An external computingdevice may connect to the computer system 700 through the network 712.In some examples, an external computing device may be an externalwebserver or a cloud computing node.

It is to be understood that the block diagram of FIG. 7 is not intendedto indicate that the computer system 700 is to include all of thecomponents shown in FIG. 7 . Rather, the computer system 700 can includeany appropriate fewer or additional components not illustrated in FIG. 7(e.g., additional memory components, embedded controllers, modules,additional network interfaces, etc.). Further, the embodiments describedherein with respect to computer system 700 may be implemented with anyappropriate logic, wherein the logic, as referred to herein, can includeany suitable hardware (e.g., a processor, an embedded controller, or anapplication specific integrated circuit, among others), software (e.g.,an application, among others), firmware, or any suitable combination ofhardware, software, and firmware, in various embodiments.

Various embodiments of the invention are described herein with referenceto the related drawings. Alternative embodiments of the invention can bedevised without departing from the scope of this invention. Variousconnections and positional relationships (e.g., over, below, adjacent,etc.) are set forth between elements in the following description and inthe drawings. These connections and/or positional relationships, unlessspecified otherwise, can be direct or indirect, and the presentinvention is not intended to be limiting in this respect. Accordingly, acoupling of entities can refer to either a direct or an indirectcoupling, and a positional relationship between entities can be a director indirect positional relationship. Moreover, the various tasks andprocess steps described herein can be incorporated into a morecomprehensive procedure or process having additional steps orfunctionality not described in detail herein.

One or more of the methods described herein can be implemented with anyor a combination of the following technologies, which are each wellknown in the art: a discrete logic circuit(s) having logic gates forimplementing logic functions upon data signals, an application specificintegrated circuit (ASIC) having appropriate combinational logic gates,a programmable gate array(s) (PGA), a field programmable gate array(FPGA), etc

For the sake of brevity, conventional techniques related to making andusing aspects of the invention may or may not be described in detailherein. In particular, various aspects of computing systems and specificcomputer programs to implement the various technical features describedherein are well known. Accordingly, in the interest of brevity, manyconventional implementation details are only mentioned briefly herein orare omitted entirely without providing the well-known system and/orprocess details.

In some embodiments, various functions or acts can take place at a givenlocation and/or in connection with the operation of one or moreapparatuses or systems. In some embodiments, a portion of a givenfunction or act can be performed at a first device or location, and theremainder of the function or act can be performed at one or moreadditional devices or locations.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a”, “an” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprises” and/or “comprising,”when used in this specification, specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, element components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thepresent disclosure has been presented for purposes of illustration anddescription, but is not intended to be exhaustive or limited to the formdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the disclosure. The embodiments were chosen and described in order tobest explain the principles of the disclosure and the practicalapplication, and to enable others of ordinary skill in the art tounderstand the disclosure for various embodiments with variousmodifications as are suited to the particular use contemplated.

The diagrams depicted herein are illustrative. There can be manyvariations to the diagram or the steps (or operations) described thereinwithout departing from the spirit of the disclosure. For instance, theactions can be performed in a differing order or actions can be added,deleted or modified. Also, the term “coupled” describes having a signalpath between two elements and does not imply a direct connection betweenthe elements with no intervening elements/connections therebetween. Allof these variations are considered a part of the present disclosure.

The following definitions and abbreviations are to be used for theinterpretation of the claims and the specification. As used herein, theterms “comprises,” “comprising,” “includes,” “including,” “has,”“having,” “contains” or “containing,” or any other variation thereof,are intended to cover a non-exclusive inclusion. For example, acomposition, a mixture, process, method, article, or apparatus thatcomprises a list of elements is not necessarily limited to only thoseelements but can include other elements not expressly listed or inherentto such composition, mixture, process, method, article, or apparatus.

Additionally, the term “exemplary” is used herein to mean “serving as anexample, instance or illustration.” Any embodiment or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments or designs. The terms “at least one”and “one or more” are understood to include any integer number greaterthan or equal to one, i.e. one, two, three, four, etc. The terms “aplurality” are understood to include any integer number greater than orequal to two, i.e. two, three, four, five, etc. The term “connection”can include both an indirect “connection” and a direct “connection.”

The terms “about,” “substantially,” “approximately,” and variationsthereof, are intended to include the degree of error associated withmeasurement of the particular quantity based upon the equipmentavailable at the time of filing the application. For example, “about”can include a range of ±8% or 5%, or 2% of a given value.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instruction by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdescribed herein.

What is claimed is:
 1. A computer-implemented method comprising:receiving system data associated with a first system, the first systemcomprising a plurality of system components, wherein the system datacomprises component data for each system component in the plurality ofsystem components; obtaining historical performance data for each systemcomponent in the plurality of system components; determining at leastone testing constraint associated with the first system; determining atest environment for the first system, the test environment comprising aplurality of test cases for the first system based on the system data,the historical performance data, and the at least one testingconstraint; and executing the test environment on the first system. 2.The computer-implemented method of claim 1, further comprising:determining a test time for test environment for the first system basedat least in part on the system data, the historical performance data,and the at least one testing constraint.
 3. The computer-implementedmethod of claim 1, wherein the at least one testing constraint comprisesa total testing time limit constraint, and the method further comprises:determining an available time period based on the total testing timelimit and the testing time for the testing environment; and collectinglearning data on the first system during the available time period. 4.The computer-implemented method of claim 3, wherein collecting thelearning data on the first system comprises: executing a learning testcase on the first system during the available time period; and adjustingone or more system parameters of the first system during the learningtest case execution.
 5. The computer-implements method of claim 4,wherein the one or more system parameters comprise at least one of avoltage adjustment, a frequency adjustment, and a temperatureadjustment.
 6. The computer-implements method of claim 1, whereindetermining the test environment for the first system comprises:generating, via a machine learning model, a first feature vectorcomprising a plurality of features extracted from the component data;plotting the first feature vector in a multi-variate feature space; anddetermining the test environment based on a location of the firstfeature vector in the multi-variate feature space.
 7. Thecomputer-implemented method of claim 1, wherein the plurality ofcomponents comprise one or more of a memory card, a cooling system, anda processor core.
 8. The computer-implemented method of claim 1, whereindetermining the at least one testing constraint comprises: determining afirst system test budget; calculating a testing time limit based on thefirst system test budget; and determining the at least one constraint asthe testing time limit.
 9. A system comprising: a memory having computerreadable instructions; and one or more processors for executing thecomputer readable instructions, the computer readable instructionscontrolling the one or more processors to perform operations comprising:receiving system data associated with a first system, the first systemcomprising a plurality of system components, wherein the system datacomprises component data for each system component in the plurality ofsystem components; obtaining historical performance data for each systemcomponent in the plurality of components; determining at least onetesting constraint associated with the first system; determining a testenvironment for the first system, the test environment comprising aplurality of test cases for the first system based on the system data,the historical performance data, and the at least one testingconstraint; and executing the test suite on the first system.
 10. Thesystem of claim 9, wherein the operations further comprise: determininga test time for test environment for the first system based at least inpart on the system data, the historical performance data, and the atleast one testing constraint.
 11. The system of claim 9, wherein the atleast one testing constraint comprises a total testing time limitconstraint, and the operations further comprise: determining anavailable time period based on the total testing time limit and thetesting time for the testing environment; and collecting learning dataon the first system during the available time period.
 12. The system ofclaim 11, wherein collecting the learning data on the first systemcomprises: executing a learning test case on the first system during theavailable time period; and adjusting one or more system parameters ofthe first system during the learning test case execution.
 13. The systemof claim 12, wherein the one or more system parameters comprise at leastone of a voltage adjustment, a frequency adjustment, and a temperatureadjustment.
 14. The system of claim 9, wherein determining the testenvironment for the first system comprises: generating, via a machinelearning model, a first feature vector comprising a plurality offeatures extracted from the component data; plotting the first featurevector in a multi-variate feature space; and determining the testenvironment based on a location of the first feature vector in themulti-variate feature space.
 15. The system of claim 9, wherein theplurality of components comprise one or more of a memory card, a coolingsystem, and a processor core.
 16. The system of claim 9, whereindetermining the at least one testing constraint comprises: determining afirst system test budget; calculating a testing time limit based on thefirst system test budget; and determining the at least one constraint asthe testing time limit.
 17. A computer program product comprising acomputer readable storage medium having program instructions embodiedtherewith, the program instructions executable by one or more processorsto cause the one or more processors to perform operations comprising:receiving system data associated with a first system, the first systemcomprising a plurality of system components, wherein the system datacomprises component data for each system component in the plurality ofsystem components; obtaining historical performance data for each systemcomponent in the plurality of components; determining at least onetesting constraint associated with the first system; determining a testenvironment for the first system, the test environment comprising aplurality of test cases for the first system based on the system data,the historical performance data, and the at least one testingconstraint; and executing the test suite on the first system.
 18. Thecomputer program product of claim 17, wherein the operations furthercomprise: determining a test time for test environment for the firstsystem based at least in part on the system data, the historicalperformance data, and the at least one testing constraint.
 19. Thecomputer program product of claim 17, wherein the at least one testingconstraint comprises a total testing time limit constraint; and theoperations further comprise: determining an available time period basedon the total testing time limit and the testing time for the testingenvironment; and collecting learning data on the first system during theavailable time period.
 20. The computer program product of claim 19,wherein collecting the learning data on the first system comprises:executing a learning test case on the first system during the availabletime period; and adjusting one or more system parameters of the firstsystem during the learning test case execution.